3 citations · 6 across the 12 of their papers we have counts for
6 papers · 1 filter
Uncertainty Quantification-Enabled Inversion of Nuclear Euclidean Responses
K. Raghavan, A. Lovato
Nuclear quantum many-body methods rely on integral transform techniques to infer properties of electroweak response functions from ground-state expectation values. Retrieving the e…
Self-supervised Learning for Anomaly Detection in Computational Workflows
Hongwei Jin, Krishnan Raghavan, George Papadimitriou +4
Anomaly detection is the task of identifying abnormal behavior of a system. Anomaly detection in computational workflows is of special interest because of its wide implications in…
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection
George Papadimitriou, Hongwei Jin, Cong Wang +5
A computational workflow, also known as workflow, consists of tasks that must be executed in a specific order to attain a specific goal. Often, in fields such as biology, chemistry…
Learning Continually on a Sequence of Graphs -- The Dynamical System Way
Krishnan Raghavan, Prasanna Balaprakash
Continual learning~(CL) is a field concerned with learning a series of inter-related task with the tasks typically defined in the sense of either regression or classification. In r…
SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs
Manisha Garg, Tyler Chang, Krishnan Raghavan
Due to the curse of dimensionality, it is often prohibitively expensive to generate deterministic space-filling designs. On the other hand, when using na{ï}ve uniform random sampli…
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
Romit Maulik, Romain Egele, Krishnan Raghavan +1
Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DN…